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Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling.

ABSTRACT: The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data.

SUBMITTER: Sun B

PROVIDER: S-EPMC8262260 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling.

Sun Bo B Bugarin-Estrada Emmanuel E Overend Lauren Elizabeth LE Walker Catherine Elizabeth CE Tucci Felicia Anna FA Bashford-Rogers Rachael Jennifer Mary RJM

Cell reports methods 20210524 1

The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with ident ...[more]

PMID: 34278374

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